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align_trans.py
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import numpy as np
import cv2
# reference facial points, a list of coordinates (x,y)
REFERENCE_FACIAL_POINTS = [
[30.29459953, 51.69630051],
[65.53179932, 51.50139999],
[48.02519989, 71.73660278],
[33.54930115, 92.3655014],
[62.72990036, 92.20410156]
]
DEFAULT_CROP_SIZE = (96, 112)
class FaceWarpException(Exception):
def __str__(self):
return 'In File {}:{}'.format(
__file__, super.__str__(self))
def get_reference_facial_points(output_size=None,
inner_padding_factor=0.0,
outer_padding=(0, 0),
default_square=False):
"""
Function:
----------
get reference 5 key points according to crop settings:
0. Set default crop_size:
if default_square:
crop_size = (112, 112)
else:
crop_size = (96, 112)
1. Pad the crop_size by inner_padding_factor in each side;
2. Resize crop_size into (output_size - outer_padding*2),
pad into output_size with outer_padding;
3. Output reference_5point;
Parameters:
----------
@output_size: (w, h) or None
size of aligned face image
@inner_padding_factor: (w_factor, h_factor)
padding factor for inner (w, h)
@outer_padding: (w_pad, h_pad)
each row is a pair of coordinates (x, y)
@default_square: True or False
if True:
default crop_size = (112, 112)
else:
default crop_size = (96, 112);
!!! make sure, if output_size is not None:
(output_size - outer_padding)
= some_scale * (default crop_size * (1.0 + inner_padding_factor))
Returns:
----------
@reference_5point: 5x2 np.array
each row is a pair of transformed coordinates (x, y)
"""
# print('\n===> get_reference_facial_points():')
# print('---> Params:')
# print(' output_size: ', output_size)
# print(' inner_padding_factor: ', inner_padding_factor)
# print(' outer_padding:', outer_padding)
# print(' default_square: ', default_square)
tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
# 0) make the inner region a square
if default_square:
size_diff = max(tmp_crop_size) - tmp_crop_size
tmp_5pts += size_diff / 2
tmp_crop_size += size_diff
# print('---> default:')
# print(' crop_size = ', tmp_crop_size)
# print(' reference_5pts = ', tmp_5pts)
if (output_size and
output_size[0] == tmp_crop_size[0] and
output_size[1] == tmp_crop_size[1]):
# print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size))
return tmp_5pts
if (inner_padding_factor == 0 and
outer_padding == (0, 0)):
if output_size is None:
# print('No paddings to do: return default reference points')
return tmp_5pts
else:
raise FaceWarpException(
'No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
# check output size
if not (0 <= inner_padding_factor <= 1.0):
raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0)
and output_size is None):
output_size = tmp_crop_size * \
(1 + inner_padding_factor * 2).astype(np.int32)
output_size += np.array(outer_padding)
# print(' deduced from paddings, output_size = ', output_size)
if not (outer_padding[0] < output_size[0]
and outer_padding[1] < output_size[1]):
raise FaceWarpException('Not (outer_padding[0] < output_size[0]'
'and outer_padding[1] < output_size[1])')
# 1) pad the inner region according inner_padding_factor
# print('---> STEP1: pad the inner region according inner_padding_factor')
if inner_padding_factor > 0:
size_diff = tmp_crop_size * inner_padding_factor * 2
tmp_5pts += size_diff / 2
tmp_crop_size += np.round(size_diff).astype(np.int32)
# print(' crop_size = ', tmp_crop_size)
# print(' reference_5pts = ', tmp_5pts)
# 2) resize the padded inner region
# print('---> STEP2: resize the padded inner region')
size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
# print(' crop_size = ', tmp_crop_size)
# print(' size_bf_outer_pad = ', size_bf_outer_pad)
if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
raise FaceWarpException('Must have (output_size - outer_padding)'
'= some_scale * (crop_size * (1.0 + inner_padding_factor)')
scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
# print(' resize scale_factor = ', scale_factor)
tmp_5pts = tmp_5pts * scale_factor
# size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
# tmp_5pts = tmp_5pts + size_diff / 2
tmp_crop_size = size_bf_outer_pad
# print(' crop_size = ', tmp_crop_size)
# print(' reference_5pts = ', tmp_5pts)
# 3) add outer_padding to make output_size
reference_5point = tmp_5pts + np.array(outer_padding)
tmp_crop_size = output_size
# print('---> STEP3: add outer_padding to make output_size')
# print(' crop_size = ', tmp_crop_size)
# print(' reference_5pts = ', tmp_5pts)
# print('===> end get_reference_facial_points\n')
return reference_5point
def get_affine_transform_matrix(src_pts, dst_pts):
"""
Function:
----------
get affine transform matrix 'tfm' from src_pts to dst_pts
Parameters:
----------
@src_pts: Kx2 np.array
source points matrix, each row is a pair of coordinates (x, y)
@dst_pts: Kx2 np.array
destination points matrix, each row is a pair of coordinates (x, y)
Returns:
----------
@tfm: 2x3 np.array
transform matrix from src_pts to dst_pts
"""
tfm = np.float32([[1, 0, 0], [0, 1, 0]])
n_pts = src_pts.shape[0]
ones = np.ones((n_pts, 1), src_pts.dtype)
src_pts_ = np.hstack([src_pts, ones])
dst_pts_ = np.hstack([dst_pts, ones])
# #print(('src_pts_:\n' + str(src_pts_))
# #print(('dst_pts_:\n' + str(dst_pts_))
A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
# #print(('np.linalg.lstsq return A: \n' + str(A))
# #print(('np.linalg.lstsq return res: \n' + str(res))
# #print(('np.linalg.lstsq return rank: \n' + str(rank))
# #print(('np.linalg.lstsq return s: \n' + str(s))
if rank == 3:
tfm = np.float32([
[A[0, 0], A[1, 0], A[2, 0]],
[A[0, 1], A[1, 1], A[2, 1]]
])
elif rank == 2:
tfm = np.float32([
[A[0, 0], A[1, 0], 0],
[A[0, 1], A[1, 1], 0]
])
return tfm
def warp_and_crop_face(src_img,
facial_pts,
reference_pts=None,
crop_size=(96, 112),
align_type='cv2_affine'):
if reference_pts is None:
if crop_size[0] == 96 and crop_size[1] == 112:
reference_pts = REFERENCE_FACIAL_POINTS
else:
default_square = False
inner_padding_factor = 0
outer_padding = (0, 0)
output_size = crop_size
reference_pts = get_reference_facial_points(output_size,
inner_padding_factor,
outer_padding,
default_square)
ref_pts = np.float32(reference_pts)
ref_pts_shp = ref_pts.shape
if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
raise FaceWarpException(
'reference_pts.shape must be (K,2) or (2,K) and K>2')
if ref_pts_shp[0] == 2:
ref_pts = ref_pts.T
src_pts = np.float32(facial_pts)
src_pts_shp = src_pts.shape
if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
raise FaceWarpException(
'facial_pts.shape must be (K,2) or (2,K) and K>2')
if src_pts_shp[0] == 2:
src_pts = src_pts.T
# #print('--->src_pts:\n', src_pts
# #print('--->ref_pts\n', ref_pts
if src_pts.shape != ref_pts.shape:
raise FaceWarpException(
'facial_pts and reference_pts must have the same shape')
if align_type is 'cv2_affine':
tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
# #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm))
else:
tfm = get_affine_transform_matrix(src_pts, ref_pts)
# #print('--->Transform matrix: '
# #print(('type(tfm):' + str(type(tfm)))
# #print(('tfm.dtype:' + str(tfm.dtype))
# #print( tfm
face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
return face_img